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Nucleic Acids Research Advance Access published online on July 15, 2008
Nucleic Acids Research, doi:10.1093/nar/gkn435
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© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Computational Biology

Hit selection with false discovery rate control in genome-scale RNAi screens

Xiaohua Douglas Zhang1,*, Pei Fen Kuan2, Marc Ferrer3, Xiaohua Shu4, Yingxue C. Liu1, Adam T. Gates5, Priya Kunapuli3, Erica M. Stec3, Min Xu5, Shane D. Marine3, Daniel J. Holder1, Berta Strulovici3, Joseph F. Heyse6 and Amy S. Espeseth7

1Biometrics Research, Merck Research Laboratories, West Point, PA 19486, 2Department of Statistics, University of Wisconsin, Madison, WI 53707, 3Automated Biotechnology, Merck Research Laboratories, North Wales, PA 19454, 4Department of Statistics, Temple University, Philadelphia, PA 19101, 5Antiviral Research, 6BARDS and 7RNA Therapeutics, Merck Research Laboratories, West Point, PA 19486, USA

*To whom correspondence should be addressed. Tel: +1 215 652 0522; Fax: +1 215 993 1835; Email: Xiaohua_zhang{at}merck.com

Received May 19, 2008. Revised June 18, 2008. Accepted June 23, 2008.

RNA interference (RNAi) is a modality in which small double-stranded RNA molecules (siRNAs) designed to lead to the degradation of specific mRNAs are introduced into cells or organisms. siRNA libraries have been developed in which siRNAs targeting virtually every gene in the human genome are designed, synthesized and are presented for introduction into cells by transfection in a microtiter plate array. These siRNAs can then be transfected into cells using high-throughput screening (HTS) methodologies. The goal of RNAi HTS is to identify a set of siRNAs that inhibit or activate defined cellular phenotypes. The commonly used analysis methods including median ± kMAD have issues about error rates in multiple hypothesis testing and plate-wise versus experiment-wise analysis. We propose a methodology based on a Bayesian framework to address these issues. Our approach allows for sharing of information across plates in a plate-wise analysis, which obviates the need for choosing either a plate-wise or experimental-wise analysis. The proposed approach incorporates information from reliable controls to achieve a higher power and a balance between the contribution from the samples and control wells. Our approach provides false discovery rate (FDR) control to address multiple testing issues and it is robust to outliers.


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This article has been cited by other articles:


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